Building Reliable LLM Agents for Knowledge Synthesis

A new paper from Technical University of Munich and Universitat Politècnica de Catalunya Barcelona explores the architecture of autonomous LLM agents, emphasizing that these systems are more than just large language models integrated into workflows. Here are the key insights:- 1. Agents ≠ Workflows Most current systems simply chain prompts or call tools. True agents plan, perceive, remember, and act, dynamically re-planning when challenges arise. 2. Perception Vision-language models (VLMs) and multimodal LLMs (MM-LLMs) act as the 'eyes and ears', merging images, text, and structured data to interpret environments such as GUIs or robotics spaces. 3. Reasoning Techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, and  Decompose, Plan in Parallel, and Merge (DPPM) allow agents to decompose tasks, reflect, and even engage in self-argumentation before taking action. 4. Memory Retrieval-Augmented Generation (RAG) supports long-term recall, while context-aware short-term memory maintains task coherence, akin to cognitive persistence, essential for genuine autonomy. 5. Execution This final step connects thought to action through multimodal control of tools, APIs, GUIs, and robotic interfaces. The takeaway? LLM agents represent cognitive architectures rather than mere chatbots. Each subsystem, perception, reasoning, memory, and action, must function together to achieve closed-loop autonomy. For those working in this field, this paper titled 'Fundamentals of Building Autonomous LLM Agents' is an interesting reading:- https://lnkd.in/dmBaXz9u #AI #AgenticAI #LLMAgents #CognitiveArchitecture #GenerativeAI #ArtificialIntelligence

Many engineers can build an AI agent. But designing an AI agent that is scalable, reliable, and truly autonomous? That’s a whole different challenge.  AI agents are more than just fancy chatbots—they are the backbone of automated workflows, intelligent decision-making, and next-gen AI systems. However, many projects fail because they overlook critical components of agent design.  So, what separates an experimental AI from a production-ready one?  This Cheat Sheet for Designing AI Agents breaks it down into 10 key pillars:  🔹 AI Failure Recovery & Debugging – Your AI will fail. The question is, can it recover? Implement self-healing mechanisms and stress testing to ensure resilience.  🔹 Scalability & Deployment – What works in a sandbox often breaks at scale. Using containerized workloads and serverless architectures ensures high availability.  🔹 Authentication & Access Control – AI agents need proper security layers. OAuth, MFA, and role-based access aren’t just best practices—they’re essential.  🔹 Data Ingestion & Processing – Real-time AI requires efficient ETL pipelines and vector storage for retrieval—structured and unstructured data must work together.  🔹 Knowledge & Context Management – AI must remember and reason across interactions. RAG (Retrieval-Augmented Generation) and structured knowledge graphs help with long-term memory.  🔹 Model Selection & Reasoning – Picking the right model isn't just about LLM size. Hybrid AI approaches (symbolic + LLM) can dramatically improve reasoning.  🔹 Action Execution & Automation – AI isn't useful if it just predicts—it must act. Multi-agent orchestration and real-world automation (Zapier, LangChain) are key.  🔹 Monitoring & Performance Optimization – AI drift and hallucinations are inevitable. Continuous tracking and retraining keeps your AI reliable.  🔹 Personalization & Adaptive Learning – AI must learn dynamically from user behavior. Reinforcement learning from human feedback (RHLF) improves responses over time.  🔹 Compliance & Ethical AI – AI must be explainable, auditable, and regulation-compliant (GDPR, HIPAA, CCPA). Otherwise, your AI can’t be trusted.  An AI agent isn’t just a model—it’s an ecosystem. Designing it well means balancing performance, reliability, security, and compliance.  The gap between an experimental AI and a production-ready AI is strategy and execution.  Which of these areas do you think is the hardest to get right?

LangChain recently published a helpful step-by-step guide on building AI agents. 🔗 How to Build an Agent –https://lnkd.in/dKKjw6Ju It covers key phases: 1. Defining realistic tasks 2. Documenting a standard operating procedure 3. Building an MVP with prompt engineering 4. Connect & Orchestrate 5. Test & Iterate 6. Deploy, Scale, and Refine While the structure is solid, one important dimension that’s often overlooked in agent design is: efficiency at scale. This is where Lean Agentic AI becomes critical—focusing on managing cost, carbon, and complexity from the very beginning. Let’s take a few examples from the blog and view them through a lean lens: 🔍 Task Definition ➡️ If the goal is to extract structured data from invoices, a lightweight OCR + regex or deterministic parser may outperform a full LLM agent in both speed and emissions. Lean principle: Use agents only when dynamic reasoning is truly required—avoid using LLMs for tasks better handled by existing rule-based or heuristic methods 📋 Operating Procedures ➡️ For a customer support agent, identify which inquiries require LLM reasoning (e.g., nuanced refund requests) and which can be resolved using static knowledge bases or templates. Lean principle: Separate deterministic steps from open-ended reasoning early to reduce unnecessary model calls. 🤖 Prompt MVP ➡️ For a lead qualification agent, use a smaller model to classify lead intent before escalating to a larger model for personalized messaging. Lean principle: Choose the best-fit model for each subtask. Optimize prompt structure and token length to reduce waste. 🔗 Tool & Data Integration ➡️ If your agent fetches the same documentation repeatedly, cache results or embed references instead of hitting APIs each time. Lean principle: Reduce external tool calls through caching, and design retry logic with strict limits and fallbacks to avoid silent loops. 🧪 Testing & Iteration ➡️ A multi-step agent performing web search, summarization, and response generation can silently grow in cost. Lean principle: Measure more than output accuracy—track retry count, token usage, latency, and API calls to uncover hidden inefficiencies. 🚀 Deployment ➡️ In a production agent, passing the entire conversation history or full documents into the model for every turn increases token usage and latency—often with diminishing returns. Lean principle: Use summarization, context distillation, or selective memory to trim inputs. Only pass what’s essential for the model to reason, respond, or act.. Lean Agentic AI is a design philosophy that brings sustainability, efficiency, and control to agent development—by treating cost, carbon, and complexity as first-class concerns. For more details, visit 👉 https://leanagenticai.com/ #AgenticAI #LeanAI #LangChain #SustainableAI #LLMOps #FinOpsAI #AIEngineering #ModelEfficiency #ToolCaching #CarbonAwareAI LangChain

Exciting Research Alert: Solving Knowledge Conflicts in Retrieval-augmented LLMs I just came across a fascinating new paper that addresses a critical challenge in retrieval-augmented large language models (LLMs): knowledge conflicts. >> The Problem When LLMs encounter conflicting information between their internal knowledge and retrieved external data, they often struggle to generate reliable responses. This creates uncertainty in decision-making and undermines the reliability of AI systems in real-world applications. >> The Solution: Swin-VIB Researchers from Nankai University, Haihe Lab of ITAI, Meta AI, and InnoPeak Technology, Inc. have developed an innovative framework called Swin-VIB that tackles this problem head-on. Swin-VIB integrates a pipeline of variational information bottleneck models to adaptively augment retrieved information and guide LLM preference during response generation. The approach is grounded in information theory and reveals a key insight: when the difference between conflicting and supplementary information is significant, LLMs confidently settle into stable preferences, but when the distinction is ambiguous, uncertainty increases dramatically. >> How It Works Under the Hood The system operates through a sliding window mechanism that: 1. Extracts attention scores from transformer decoder blocks 2. Compresses these representations through an encoder-decoder bottleneck structure 3. Adaptively accepts or rejects external context based on uncertainty predictions The framework uses a three-step process: - Retrieval: Gathering relevant contexts from external knowledge - Augmentation: Using trained bottlenecks to perform sliding-window inference - Response generation: Producing reliable outputs even with conflicting information >> Impressive Results The results speak for themselves: - Improved single-choice task accuracy by at least 7.54% over competitive baselines - Enhanced performance across single-choice, open-ended QA, and retrieval-augmented generation (RAG) tasks - Significantly reduced output uncertainty in LLMs - Maintained high-quality response generation without compromising authenticity or coherence This research represents a significant step forward in making retrieval-augmented LLMs more reliable in the wild, especially when dealing with misinformation, biases, or outdated knowledge. What are your thoughts on this approach to handling knowledge conflicts in AI systems? Have you encountered similar challenges in your work with LLMs?

Thrilled to share our latest paper – 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠𝐁𝐚𝐧𝐤: 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐥𝐟-𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐌𝐞𝐦𝐨𝐫𝐲 🚀 For LLM agents to succeed in persistent, end-to-end real-world roles without human intervention, they can't afford to repeat the same mistakes. 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠𝐁𝐚𝐧𝐤 is a new memory framework that allows agents to learn from both successful and failed experiences, distilling them into generalizable reasoning strategies and achieving self-evolution at scale. Key Findings: • ReasoningBank consistently outperforms existing memory systems (like raw trajectories or successful routines) across web browsing (WebArena, Mind2Web) and software engineering (SWE-Bench-Verified) benchmarks. • Unlike previous methods, ReasoningBank successfully transforms failures into constructive signals, enabling more robust generalization • ReasoningBank evolves agent strategies from low-level actions to high-level, complex reasoning over time. • We believe this establishes memory-driven experience scaling as a critical new dimension for building adaptive, lifelong-learning agents. Paper: https://lnkd.in/gV8NjCsn Authors: Siru Ouyang, Jun Yan, I-Hung Hsu, Yanfei C., Ke Jiang, Zifeng Wang, Rujun Han, Long Le, Samira Daruki, Xiangru Tang, Vishy Tirumalashetty, George Lee, Mahsan Rofouei, Hangfei Lin, Jiawei Han, Chen-Yu Lee, Tomas Pfister #LLM #Agents #Memory #Reasoning #SelfEvolution #TestTimeScaling

What if we added symbolic logic and multi-agent planning to GraphRAG, for retrieving and reasoning over chemistry knowledge? That’s MOSES. GraphRAG pairs LLMs with knowledge graphs (KG) to improve retrieval and grounding through entities and relationships. But in scientific domains, where relationships are multiscale, logic-heavy, and often implicit, LLMs need additional scaffolding. Two mechanisms help: 🔹Ontology as a compass: formalize hierarchy, properties and constraints to enable precise querying and logical inference. 🔹Multi-agent systems as workers: divide tasks into preprocessing, planning, validation, and refinement steps. MOSES (Multi-agent Ontology System for Explainable Knowledge Synthesis) combines both for chemistry.  Its ontology-based, agentic workflow: 1️⃣ Generates hypothesis: proposes likely mechanisms, relevant entities, and the structure of a complete answer. 2️⃣ Parses query: extracts intent and key entities, maps them to ontology classes, and classifies the query type. 3️⃣ Plans and executes: formulates a detailed execution plan and retrieve structured information from the KG. 4️⃣ Validates & iterates: Checks and refines results for completeness and logical coherence; format final output. This is less about producing "better answers" than explainable answers, with a visible trail of what was asked, what was found, how concepts were connected, and where evidence originates. Based on consistent feedback from scientists, this traceability and explainability are what build trust, and make LLMs a credible partner in scientific discovery. 📄 MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning, ChemRxiv, October 1, 2025 🔗 https://lnkd.in/e89vB6_V

AI agents are widely misunderstood due to their broad scope. To clarify, let's derive their capabilities step-by-step from LLM first principles... [Level 0] Standard LLM: An LLM takes text as input (prompt) and generates text as output, relying solely on its internal knowledge base (without external information or tools) to solve problems. We may also use reasoning-style LLMs (or CoT prompting) to elicit a reasoning trajectory, allowing more complex reasoning problems to be solved. [Level 1] Tool use: Relying upon an LLM’s internal knowledge base is risky—LLMs have a fixed knowledge cutoff date and a tendency to hallucinate. Instead, we can teach an LLM how to use tools (by generating structured API calls), allowing the model to retrieve useful info and even solve sub-tasks with more specialized / reliable tools. Tool calls are just structured sequences of text that the model learns to insert directly into its token stream! [Level 2] Orchestration: Complex problems are hard for an LLM to solve in a single step. Instead, we can use an agentic framework like ReAct that allows an LLM to plan how a problem should be solved and sequentially solve it. In ReAct, the LLM solves a problem as follows: 1. Observe the current state. 2. Think (with a chain of thought) about what to do next. 3. Take some action (e.g., output an answer, call an API, lookup info, etc.). 4. Repeat. Decomposing and solving problems is intricately related to tool usage and reasoning; e.g., the LLM may rely upon tools or use reasoning models to create a plan for solving a problem. [Level 3] Autonomy: The above framework outlines key functionalities of AI agents. We can make such a system more capable by providing a greater level of autonomy. For example, we can allow the agent to take concrete actions on our behalf (e.g., buying something, sending an email, etc.) or run in the background (i.e., instead of being directly triggered by a user’s prompt). AI agent spectrum: Combining these concepts, we can create an agent system that: - Runs asynchronously without any human input. - Uses reasoning LLMs to formulate plans. - Uses a standard LLM to synthesize info or think. - Takes actions in the external world on our behalf. - Retrieves info via the Google search API (or any other tool). Different tools and styles of LLMs provide agent systems with many capabilities-the crux of agent systems is seamlessly orchestrating these components. But, an agent system may or may not use all of these functionalities; e.g., both a basic tool-use LLM and the above system can be considered “agentic”.

Structured reliability for unstructured intelligence. This week, we had the privilege of hosting Liana Patel, a Stanford PhD researcher and creator of LOTUS, an open-source system for LLM-powered data processing with accuracy guarantees. Key Learnings: - LOTUS introduces semantic operators such as filter, join, top-k, and aggregate that extend pandas with relational-style operators for unstructured data. - Each operator is parameterized by natural language expressions like “the paper title is the funniest,” turning LLM reasoning into declarative queries. - Under the hood, LOTUS handles batching, context-length management, and cost-based planning to keep LLM pipelines efficient and accurate. - Its optimizer uses model cascades and sampling-based thresholds to guarantee precision and recall targets while reducing cost by orders of magnitude. It bridges two worlds: the rigor of relational systems and the flexibility of language models, allowing users to bring database-style declarative programming to unstructured data. From analyzing research papers and sales call transcripts to building agent-trace dashboards, Lotus shows how structured reasoning can finally meet unstructured intelligence. Follow Liana: https://lnkd.in/gd7zMEYD Lotus Repo: https://lnkd.in/gG4WTxbg Paper: https://lnkd.in/gEusnP64 #AI #LLM #DataSystems #Research #Databases #OpenSource

AI agents don’t fail because the model is wrong. They fail because the system isn’t built for the real world. Cohere’s agent architecture paper doesn’t waste time with buzzwords. It lays out a crisp, modular blueprint for enterprises serious about operational AI. Not a prototype. Not a demo. Production-grade agent systems. Here’s what stands out: → The architecture is not about one LLM doing everything. → It’s a runtime system with five cooperating components: • Orchestrator - manages the interaction logic • Tools - external APIs or internal functions • Memory - context store for working state • Retriever - pulls relevant knowledge • Executor - translates plans into action This is what separates a GenAI toy from an enterprise agent. Cohere makes the key point most vendors skip: You don’t need a smart agent. You need a reliable, auditable, modular agent. Most enterprise failures come from: • Lack of separation between plan and execution • Inability to trace decisions • Hardcoded prompts instead of dynamic calls • No fallback when tools fail • No grounding in business-specific data And the best line in the report? “𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐮𝐧𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐚𝐜𝐭𝐨𝐫𝐬 𝐢𝐧 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦. 𝐓𝐡𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐚𝐜𝐜𝐨𝐮𝐧𝐭 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬.” Exactly. That’s how you build systems that survive Monday morning escalations. CIOs: don’t benchmark demos; benchmark recovery. How does the agent behave when tools fail? When the LLM hallucinates? When data is missing? That’s the bar. If your agent can’t handle exceptions, it won’t survive production. If your architecture can’t adapt, it won’t scale. This isn’t chatbot design. This is system engineering for autonomous workflows. And the enterprises that get it right will build a moat others can’t cross.